GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

doi:10.1109/TPAMI.2019.2957464

	GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
	Huang, Lianghua 3,4; Zhao, Xin 3,4; Huang, Kaiqi 1,2,3,4
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2021-05-01
卷号	43 期号:5 页码:1562-1577
关键词	Training Object tracking Databases Protocols Benchmark testing Servers Object tracking benchmark dataset performance evaluation
ISSN号	0162-8828
DOI	10.1109/TPAMI.2019.2957464
通讯作者	Huang, Lianghua(huanglianghua2017@ia.ac.cn)
英文摘要	We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure [1] and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts [19], [20], [23], [26]. By releasing the large high-diversity database, we aim to provide a unified training and evaluation platform for the development of class-agnostic, generic purposed short-term trackers. The features of GOT-10k and the contributions of this article are summarized in the following. (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) GOT-10k offers additional labels such as motion classes and object visible ratios, facilitating the development of motion-aware and occlusion-aware trackers. (5) We conduct extensive tracking experiments with 39 typical tracking algorithms and their variants on GOT-10k and analyze their results in this paper. (6) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it.
资助项目	National Key Research and Development Program of China[2016YFB1001001] ; National Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61602485] ; National Natural Science Foundation of China[61673375] ; Projects of Chinese Academy of Science[QYZDB-SSW-JSC006]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000637533800007
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44233]
专题	智能系统与工程
通讯作者	Huang, Lianghua
作者单位	1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Res Intelligent Syst & Engn, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Lianghua,Zhao, Xin,Huang, Kaiqi. GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(5):1562-1577.
APA	Huang, Lianghua,Zhao, Xin,&Huang, Kaiqi.(2021).GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(5),1562-1577.
MLA	Huang, Lianghua,et al."GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.5(2021):1562-1577.